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tech.ml-base


auto-gridsearch-optionsclj

(auto-gridsearch-options system-name options)
source

gridsearchclj

(gridsearch system-name->options-seq
            feature-keys
            label-keys
            loss-fn
            dataset
            &
            {:keys [parallelism top-n gridsearch-depth k-fold scalar-labels?]
             :or {parallelism (.availableProcessors (Runtime/getRuntime))
                  top-n 5
                  gridsearch-depth 50
                  k-fold 5}
             :as options})

Gridsearch these system/option pairs by this dataset, averaging the errors across k-folds and taking the lowest top-n options. We are breaking out of 'simple' and into 'easy' here, this is pretty opinionated. The point is to make 80% of the cases work great on the first try.

Gridsearch these system/option pairs by this dataset, averaging the errors
  across k-folds and taking the lowest top-n options.
We are breaking out of 'simple' and into 'easy' here, this is pretty
opinionated.  The point is to make 80% of the cases work great on the
first try.
sourceraw docstring

predictclj

(predict model dataset)
source

trainclj

(train system-name feature-keys label-keys options dataset)
source

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